File size: 1,970 Bytes
d2f661a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | import numpy as np
import torch.nn as nn
from ..blocks.resnet import ResBlock3D
from ..utils import activation, normalization
class SimpleConvEncoder(nn.Sequential):
def __init__(self, in_dim=1, levels=2, min_ch=64):
sequence = []
channels = np.hstack([
in_dim,
(8**np.arange(1,levels+1)).clip(min=min_ch)
])
for i in range(levels):
in_channels = int(channels[i])
out_channels = int(channels[i+1])
res_kernel_size = (3,3,3) if i == 0 else (1,3,3)
res_block = ResBlock3D(
in_channels, out_channels,
kernel_size=res_kernel_size,
norm_kwargs={"num_groups": 1}
)
sequence.append(res_block)
downsample = nn.Conv3d(out_channels, out_channels,
kernel_size=(2,2,2), stride=(2,2,2))
sequence.append(downsample)
in_channels = out_channels
super().__init__(*sequence)
class SimpleConvDecoder(nn.Sequential):
def __init__(self, in_dim=1, levels=2, min_ch=64):
sequence = []
channels = np.hstack([
in_dim,
(8**np.arange(1,levels+1)).clip(min=min_ch)
])
for i in reversed(list(range(levels))):
in_channels = int(channels[i+1])
out_channels = int(channels[i])
upsample = nn.ConvTranspose3d(in_channels, in_channels,
kernel_size=(2,2,2), stride=(2,2,2))
sequence.append(upsample)
res_kernel_size = (3,3,3) if (i == 0) else (1,3,3)
res_block = ResBlock3D(
in_channels, out_channels,
kernel_size=res_kernel_size,
norm_kwargs={"num_groups": 1}
)
sequence.append(res_block)
in_channels = out_channels
super().__init__(*sequence)
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